integrating evaluation grid method and fuzzy quality...

Research ArticleIntegrating Evaluation Grid Method and Fuzzy QualityFunction Deployment to New Product Development

Xinhui Kang Minggang Yang YixiangWu and Bingqing Ni

School of Art Design and Media East China University of Science and Technology No 130 Meilong RoadXuhui District Shanghai 200237 China

Correspondence should be addressed to Xinhui Kang nbukxh163com

Received 1 October 2017 Revised 29 March 2018 Accepted 19 April 2018 Published 30 July 2018

Academic Editor Francesco Ferrise

Copyright copy 2018 Xinhui Kang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The success of a new product is usually determined not by whether it includes high-end technology but by whether it meetsconsumer expectations especially key Kansei demands This article aims to evaluate attractive factors (Kansei words) and convertthem to design elements tomake products stand out in the global competitionThe evaluation grid method (EGM) is an importantresearch method of Miryoku engineering The method can build qualitative relations among consumersrsquo attractive factors anddesign elements The quality function deployment (QFD) is a quantitative method which converts customer requirements intoengineering characteristics using the House of Quality Matrix The QFD together with the concept of fuzziness can objectivelymeasure questionnaires made by experts Accordingly this paper proposes a systematic approach that integrates the EGM togetherwith the fuzzy QFD for the development of new products The fuzzy Kano model combined with the fuzzy analytic hierarchyprocess (AHP) is developed to determine the priority of the development of attractive factors This empirical study uses minicarsas an example to verify the feasibility and validity of the approach The results are expected to help designers to increase designefficiency and improve consumer satisfaction of new products

1 Introduction

With improvements in modern technologies and sophisti-catedmanufacturing product life cycles have been shortenedCorporations have to keep developing new products to adaptto rapidly changing market demands and sustain operationin the market However most traditional designers tend tomake decisions based on previous experience and personalpreferences They usually neglect the voice of customers notonly wasting precious time for designing new products butalso increasing risks Nijssen and Frambach [1] point outthat new product development (NPD) faces a high failurerate where the success rate is generally not larger than 60Accordingly we must fully understand the customer needsof the target market and continue creating superior value forcustomers in order to bring new products to the society withrequired novelty and changes [2]

These days more attention is paid to productsrsquo emotionalconnotations and aesthetic values rather than functionalabilities and prices It has become increasingly vital to take

emotional elements into consideration during NPD as emo-tions nowplaymore important rolewhenweinteractwith pro-ducts in our daily life [3] Newman [4] mentions three typesof consumer demands for products related to basic functionattributes convenient function attributes and psychologicalsatisfaction attributes Consumers expect their psychologicalneeds to be met when they purchase a product According toJordanrsquos Hierarchy of User Needs the meaning of a productfirst comes from its functionality then usability and finallypleasure This is why usersrsquo expectations regarding a productcome from practical and emotional needs [5] Jensen [6]points out that actually we search for stories friendshipcare a lifestyle and character when we shop that is wepurchase emotionsTherefore products benefit frombeing inline with consumersrsquo emotional needs when it comes to theirmarket shares Besides a dominant factor that determines thesuccess of a new product is whether it has a form that suitscustomersrsquo preferences [7] The product form contributes toconsumersrsquo first impression It triggers their sense of iden-tification and emotional resonance which results in their

HindawiMathematical Problems in EngineeringVolume 2018 Article ID 2451470 15 pageshttpsdoiorg10115520182451470

2 Mathematical Problems in Engineering

purchase decisions Nagamachi a Japanese scholar first sug-gested taking into account customersrsquo senses and feelings(Kansei) in the 1970s Further Kansei engineering was de-veloped as a comprehensive consumer oriented technologyfor the NPD [8] Researchers consider five senses and theirquantification to study consumersrsquo emotions Tanoue et al [9]utilize Kansei engineering to evaluate automotive interiorimages especially roominess and oppressiveness Schutte andEklund [10] apply Kansei engineering to design rockerswitches for work-vehicles Nagamachi [11] presents and dis-cusses Kansei engineering thus realizing several new Kanseiproducts so far Vieira et al [12] use Kansei engineeringfor the design of in-vehicle rubber keypads Miryoku engi-neering is a part of Kansei engineering since 1998 when theJapan Society of Kansei Engineering (JSKE) was establishedIt mainly discovers user preferences which come from prod-uct values and charms determined by product attributes [13]Kansei engineering quantifies humanrsquos perception and feelingwith the method of engineering and then transforms con-sumersrsquo feeling into the technology of new products How-everMiryoku engineering discusses the relationship betweenconsumersrsquo abstract reasons for specific products and con-crete characteristics with the qualitative way of face-to-faceinterview Moreover the personal hierarchical diagram isformed by laddering ldquoMiryokurdquo in Japanese means ldquothepower of attractivenessrdquo Product attractiveness is primary toconsumers Based on in-depth interviewsMiryoku engineer-ing helps interviewees to take various appealing factors fromsamples and transfer the productrsquos Kansei attractiveness intoits form with the goal to improve customer satisfaction andmarket reception

As an important research method of Miryoku engineer-ing the objective of the evaluation grid method (EGM) is tobuild a hierarchical structure between abstract reasons andconcrete design elements which is usually hard to captureThe EGM has been successfully applied to product designgame development festival industries and other researchfields [14ndash20] Nevertheless there are two urgent issues thatneed to be solved First the screening of words among ob-tained upper-level attractive factors (Kansei words) is basedon their number of appearance by respondents where fre-quencies only show the degrees to which words are relatedto the product but do not reflect their importance Secondnumerical relationships between upper-level attractive fac-tors and lower-level design elements are usually based onmultiple linear regressions such as the Quantification TheoryType I (QT-I) used for mapping but Kansei data does notalways have linear features assumed when using the normaldistribution [21] A linear regression can lead to deviation andmisjudgment in the consequent design process

In the view of the above arguments the present studyintends to import the attractive factors of the EGM into thefuzzy quality function deployment (QFD) system to acquirethe best combination of crucial Kansei needs and designelements First we adopt the hierarchical diagram obtainedby the EGM to the House of Quality (HoQ) and secondwe obtain the crucial attractive factors by combining thetwo-dimensional fuzzy Kano model and the fuzzy analytichierarchy process (AHP) Design teams are recommended

to gather limited resources and pay attention to these resultsin order to maximize overall customer satisfaction They canexcavate the most important design elements by comparingcrucial attractive factors and design elements obtained bythe HoQ This study aims to discover customersrsquo feelingsand translate them into form elements that enhance positiveeffects and increase the attraction of product appearanceThe proposed method is based on the fuzzy theory usedfor uncertain consumer requirements (CRs) We build anobjective semantic questionnaire to learn real customer Kan-sei demands to cut losses resulting from colloquial phrasesFurthermore a brief comparison of the distinction betweenthis study and previous studies has been presented in Table 1The main contribution of this work can be summarized asfollows

(i) A hybrid framework is proposed to effectively convertthe voice of customers (attractive factors) into thevoice of engineers (design elements) thus reducingthe generation gap between both sides

(ii) Via combination of a two-dimensional model and theweight analysis method consumersrsquo attractive factorsto target products can be evaluated more accurately

(iii) Our survey data assist enterprises to identify custom-ersrsquo expectations thus finally integrating these intothe NPD

The remainder of this article is organized as followsSection 2 provides a brief literature review In Section 3 thenewly proposed method is explained in detail In Section 4 acase study for minicars is provided to demonstrate the effec-tiveness of the developed approach The paper is concludedin Section 5

2 Literature Review

21Miryoku Engineering and Evaluation GridMethod Miry-oku engineering proposed by a Japanese scholar MasatoUjigawa and his group in 1991 aims to create attractive pro-ducts and spaces by adopting a design philosophy centered atconsumer preferences [13] The charm traits of a product canbe captured through learning the way in which consumerschoose products and studying successful examples of productdesign so that an attractive design can be created [22] How-ever Miryoku is a vague concept that cannot be measuredby specific tools For this reason in this research we analyzecustomersrsquo attractive factors by layers with the aid of theEGM

The EGM is an important research approach of Miryokuengineering proposed by Japanese scholars Junichiro Sanuiand Masao Inui based on the psychologistrsquos Repertory GridMethod of Kelly in 1986 [23] with two added steps (Figure 1)One step compares and evaluates samples in terms of meritsand demerits during interviews to obtain original evaluationitems through intervieweesrsquo perspectives and senses Theother step obtains abstract reasons (upper-level) which con-nect usersrsquo emotion attitudes and product concrete conditions(lower-level) By doing this repeatedly we can obtain a hier-archical diagram of consumersrsquo inner feelings towards sam-ples [24]

Mathematical Problems in Engineering 3

Table1Abriefcom

paris

onof

thed

istinctionbetweenthisstu

dyandprevious

studies

Publication

Expertinterview

Emotionalevaluation

Functio

nalevaluation

Transla

testechn

iques

Marketsegmentatio

nTh

ispaper

EGM

FuzzyKa

nomod

elFuzzy

AHP

FuzzyQFD

Shen

etal[15]

EGM

KEQT-I

Shen

[17]

EGM

QT-I

HoandHou

[16]

EGM

QT-I

Userb

ackgroun

dsWang[28]

AHPTR

IZQFD

Userp

erceptions

WangandHsueh

[46]

AHPKa

nomod

elDEM

ATEL

Custo

mer

perceptio

nsWangandWang[54]

FuzzyKa

nomod

elFuzzy

AHP

Affo

rdableprices

Chen

andCh

uang

[50]

KEK

anomod

elEntropy

QT-I

Alptekin[63]

Focusg

roup

FuzzyAHPFC

PUserp

rofiles

Yadavetal[52]

FuzzyKa

nomod

elQFD

Shiehetal[67]

POSFu

zzyAHP

QT-I

Wang[7]

KERS

TANFIS

Linetal[39]

Focusg

roup

Kano

mod

elFu

zzyQFD

Jietal[45]

Kano

mod

elQFD

NotesK

EKa

nseiengineeringTR

IZtheoryofinventivep

roblem

solvingDEM

ATEL

decision

-makingtrialand

evaluatio

nlabo

ratoryFCP

fuzzy

comprom

iseprogrammingPO

SPareto

optim

alsolutio

nsR

ST

roughsettheoryandANFISAd

aptiv

eNetwo

rk-Based

FuzzyInferenceS

ystem


Figure 1 A three-level hierarchical diagram for a single evaluation project

The EGM have been applied to a large number of fieldssuch as user experience interior design cultural and creativeindustries and product design For instance Park et al [14]study key user requirements in mobile hospital applicationsby using the EGMand discuss how to assess and improve userdesign in mobile and wireless environments Shen et al [15]use the EGM for expert evaluation to explore the appeal of theCrossover B-Car interior from the perspectives of usabilityand functionality Ho and Hou [16] utilize Miryoku engi-neering methods combined with a Kansei interface model toexamine the relationship between attractive icons and userswhere the EGM is used to evaluate icons and the QT-I is usedto analyze the influence of design elements in icons Shen [17]explores the sociocultural appeal of social networking service(SNS) game content Chen et al [18] apply the EGM andQT-I to explore the appeal of Facebook SNS games from theperspectives of game usability and functionality facilitated bythe Internet Using the Taiwan Lantern Festival as a sampleMa and Tseng [19] adopt the EGM and QT-I to systematicallyanalyze the fuzzy perception of human Kansei factors Theauthors select attractive factors before weight analysis for theevaluation of the attractiveness of the festival 3C products areused as representatives and the EGM inMiryoku engineeringis adopted for constructing the structure of consumersrsquopreferences for newproducts Eventually a design strategy forattractiveness of new products is established [20]

In this study we obtain customersrsquo emotional preferencesto analyze the semantic structure in detail We use theEGM to collect the attractive factors of minicars for furtherquantitative research

22 Fuzzy Quality Function Deployment The concept of theQFD was first introduced by Akao in 1966 It is a struc-tured methodology used to discuss CRs on products andservices Besides it is also a process of transforming CRsinto engineering characteristics (ECs) [25] Cristiano et al[26] successfully apply QFD to obtain the key factors of newproducts that meet customer expectations Kowalska et al[27] implement QFD for a quality analysis of confectioneryproducts The development of the QFD has taken placefor fifty years and it has been already widely applied toseveral academic fields and industries [28ndash33] The HoQ(Figure 2) is the core of the QFD which performs a basic andstrategic role in the QFD Products and services depend oncustomersrsquo needs during the process when HoQ is appliedThe voice of customers is compared to the voice of engineersto build priority According to Hauser and Clausing [34] theapplication of the QFD reduces product development time by50 and costs by 30 Accordingly this studymainly focuseson the collection of customer needs and conversion of theminto product design parameters

In the traditional QFD the impreciseness of consumersrsquosubjective assessments is usually ignored since variables are

supposed to be crisp values In order to address this issuesome studies combine the fuzzy set theory and QFD toobtain customersrsquo true thoughts Khoo and Ho [35] processlinguistics and identify variables with symmetrical triangularfuzzy numbers (TFNs) for theQFD systemand then apply theldquovoice of customersrdquo containing ambiguity and multiplicityof meaning to design decisions Chan and Wu [36] utilizesymmetrical TFNs to capture the vagueness of linguisticassessments in the HoQ process Wu [37] proposes a fuzzyHoQmodel and the QFD for the fuzzy regression estimationproblem Zaim et al [38] adopt the fuzzy QFD and ANPweighted crisp for product development Lin et al [39]introduce an integrative framework of the Kano model intothe fuzzy QFD for Taiwanese Ban-Doh banquet cultureVinodh et al [40] apply the fuzzy QFD to the sustainabledesign of consumer electronic products Following theseideas this study uses fuzzy numbers to replace crisp valuesin matrix operations to enhance the reliability of results forobjective measurement

23 Two-Dimensional Quality Model In order to overcomethe disadvantages of one-dimensional quality cognitionKano et al [41] suggest a two-dimensional model of quality toemphasize the importance of interaction between productquality and overall customer satisfaction from the asym-metric nonlinear point of view From this we can learn therelationship between productsrsquo attributes and customer sat-isfaction through the classification of attributes Berger et al[42] employ theKanomodel to understand customer-definedquality Yadav et al [43] integrate both Kano and robustdesign approach for aesthetical car profile design Velikovaet al [44] apply the Kano model to identify the overallsatisfaction of a wine festival The Kano model has also beenwidely used in product design real estate sales hotel servicesand some other fields [45ndash50]

The traditional Kano survey method however merelyallows single choices from five standard answers This pre-vents people from expressing the diversity of their logicalthinking Moreover customersrsquo feelings can easily be influ-enced as the decision-making environment is quite compli-cated which leads to wrong classification results Accord-ingly Lee and Huang [51] modify the traditional Kano ques-tionnaire using the fuzzy theory allowing giving multipleanswers to acquire more responsive results Yadav et al [52]integrate the fuzzy Kano model and QFD to explore theprioritization of the aesthetic attributes of car profiles andmerge customersrsquo aesthetic feelings into the product designprocess Taking smart pads as an example Wang [53] incor-porates customer satisfaction into the decision-making pro-cess of product configuration from the fuzzyKanomodel per-spective Wang andWang [54] employ the fuzzy Kano modelto elicit the customer perceptions of the optional attributes


Figure 2The house of quality

of smart cameras It is a well-accepted fact from literaturethat the fuzzy Kano model enables more elastic responsesto questionnaires on the semantic scale to make them moreconvenient for interviewees to show multiple feelings whichnot only can assist in knowing customersrsquo inner feelings butcan also help to shorten the NPD period Unfortunatelythe fuzzy Kano model cannot prioritize attributes within thesame category and can have difficulty in accurate classifica-tion of CRs when there is only small statistical differencebetween two or more categories [55] Accordingly this studysupposes that attributes form amulticriteria decision-makingproblem and uses the weight approach to calculate their rawweights and then adjust the weights in accordance with thefuzzyKanomodel classification to determine priority ratings

24 Fuzzy Analytic Hierarchy Process The analytic hierarchyprocess (AHP) a decision-making method developed bySaaty [56] in 1971 is mainly used in cases with uncertaintyand decision problems with various evaluation criteria Zhuet al [57] present the AHP to calculate the relative weight ofeach evaluation criterion for design concept alternatives Satoet al [58] integrate both the AHP and the marginal analysisapproach for build-to-order products FurthermoreOoi et al[59] use the AHP to overcome the ambiguities involved inassessing the relative importance weightings of target proper-ties in multi-objective molecular design problems Althoughthe AHP is easy to use experts can be affected by subjectivecognitive factors in assessment The fuzzy set theory firstproposed by Zadeh [60] describes fuzzy phenomena in dailylife This can be thought of as an extension of traditionalsets in which each element must either be in the set or notin the set (ie 0 or 1) Accordingly Laarhoven and Pedrycz[61] propose to use TFNs of the fuzzy set theory directlyin the pairwise comparison matrix of the AHP in order toovercome expertsrsquo difficulty in judgment due to inevitableambiguous words the method is called the fuzzy AHP In thefollowing research Buckley [62] applies the fuzzy set the-ory to the traditional AHP and generalizes the notion ofconsistency in fuzzy matrices Alptekin [63] provides end-users with a decision support framework based on the fuzzyAHP and fuzzy compromise programming methodologiesin order to select optimal digital cameras according to theirpreferences Cho and Lee [64] classify the success factors of

the commercialization of new products and analyze whichfactors should be primarily considered by the fuzzy AHP ap-proach Yeh et al [65] use the fuzzy AHP and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) toidentify critical factors in the NPD Sabaghi et al [66] employthe fuzzy AHP together with Shannonrsquos entropy formula todetermine the relative importance of each element in a fuzzy-inference system Shieh et al [67] employ the fuzzy AHP togain optimal car profile design solutions

The research above has made it clear that the best featureof the fuzzy AHP is the ability to obtain systematically com-plex relations among assessment criteria with a hierarchicalstructure which leads to excellent results in the evaluation ofdesign processes and emotional dimensions Accordinglythis study uses the fuzzyAHP to establish a hierarchical struc-ture of the attractive factors of minicars and integrates inter-vieweesrsquo individual opinions to obtain the priority weights ofcriteria

3 The Proposed Integrative Method

As shown in Figure 3 this study proposes a systematic meth-od of creating attractive new products using the EGM com-bined with the fuzzy QFD to obtain accurately relationshipsbetween consumersrsquo visual appeals and specific product formelements The experiment can be divided into three stages asfollows

(i) First we conduct in-depth interviews with highlyinvolved groups using the EGM analyze productsrsquoattractive factors extract upper-level abstract reasonsmiddle-level original evaluation items and low-levelconcrete conditions and obtain the correspondingthree-level evaluation structure diagram Then weimport the hierarchical diagram to the HoQ with theupper attractive factors at the left side of the CRs facetand the original items and concrete conditions at thetop of the HoQ

(ii) Second at the left side of theHoQwedivide attractivefactors according to quality attributes using the fuzzyKano model to determine attractive attributes one-dimensional attributes and must-be attributes thathave remarkable influence on customer satisfaction


Figure 3 The proposed integrative method

and analyze customersrsquo true needs therefrom Thenafter calculating the raw weights of factors the fuzzyAHP is used to adjust the weights in accordance withthe results on attribute determination where the finaldecision regarding what key factors to be givenpriority in development is made

(iii) Finally by sorting the obtained weights the attractivefactors are translated into design elements to meetcustomer needs after comparing them using the HoQmatrix

31 Phase One Assessing the Attractive Factors of the ProductsAt this stage we commit to the product domain and collectexperimental samples After that the participants make com-ments regarding sample preferences and learn their originalevaluation items in the domain We then guide them to talkabout abstract feelings and concrete conditions by providingadditional questions Finally we draw an overall evaluationhierarchical diagram Experimental results can be obtainedthrough direct qualitative reference or quantitative analysisto grasp clearly the real opinion of customers

The procedure of the EGM can be briefly described asfollows

(1) Prepare relevant questions and pictures needed forthe interview

(2) Conduct one-to-one interviews and ask every inter-viewee to divide the pictures into two groups thosethey like and dislike

(3) Remove the ones they dislike(4) Let the interviewees provide reasons for their prefer-

ences in order to build their original evaluation items(5) Ask them about abstract reasons (upper-level) and

details (lower-level) they like in the light of theoriginal evaluation items

(6) Gather all notes from the interviews and connect allcorresponding items using straight lines to show hier-archical relations

(7) Due to the excessive Kansei words at the upper-levelwe group the oneswith similar contents and attributesusing the KJ method name such groups and repeatthis step until no groups can be created any moreAfter this process the attractive factors are obtained

32 Phase Two Determining the Crucial Attractive FactorsThe Kano attribute classification is conventionally under-taken by using a number of bidirectional questions to differattributes using cross-pairs [68] The questions on bothsides are opposite statements One of these can be ldquowhenattributes are sufficient what about customer satisfactionrdquoand the other ldquowhen attributes are insufficient what aboutcustomer satisfactionrdquo The answer options include ldquodissat-isfiedrdquo ldquolive with itrdquo ldquoindifferentrdquo ldquomust-berdquo and ldquosatisfiedrdquoUltimately we can determine the product attributes throughthe 25 combinations in the evaluation table (Table 2) Theyare ldquoattractiverdquo ldquoone-dimensionalrdquo ldquomust-berdquo ldquoindifferentrdquoldquoreverserdquo and ldquoquestionablerdquo


Table 2 Kano evaluation table

Criteriaattributes InsufficiencySatisfied It must be that way It is Indifferent I can live with it Dissatisfied

Sufficiency

Satisfied Q A A A OIt must be that way R I I I MIt is Indifferent R I I I MI can live with it R I I I MDissatisfied R R R R Q

Notes A attractive O one-dimensional M must-be I indifference R reversal and Q questionable

Table 3 TFNs in fuzzy AHP

Linguistic Scale Fuzzy NumberEqually important 1 = (1 1 2)Slightly important 3 = (2 3 4)important 5 = (4 5 6)Very important 7 = (6 7 8)Extremely important 9 = (8 9 9)Intermediate Value inserted between two continuous dimensions 2 = (1 2 3) 4 = (3 4 5) 6 = (5 6 7) 8 = (7 8 9)

However multiple choices are allowed in the fuzzy Kanomodel According to the fuzzy feelings of interviewees wecan bidirectionally assign a percentage to the corresponding1st to 3rd answers with 1 being their total sum For instancethe answers ldquosufficiencyrdquo and ldquoinsufficiencyrdquo to a productrsquosattractive factor can be described as 119904119906119891 = (07 03 0 0 0)and 119894119899119904 = (0 0 01 03 06) We can obtain a 5 times 5 fuzzyrelation matrix 119878 throughmatrix multiplication (119904119906119891)119905otimes(119894119899119904)The superscript 119905 denotes the transpose operation

119878 =[[[[[[[[[

0 0 007 021 0420 0 003 009 0180 0 0 0 00 0 0 0 00 0 0 0 0

]]]]]]]]]

(1)

Based on Table 2rsquos identifying two-dimensional attributeclassification in matrix S we can select something as follows

119879 = 028119860 018119872 042119874 012119868 0119877 (2)

In order to discover more pleasant classification weusually use the standard 120572 - cut to obtain 119879ℎ120572 Let thethreshold value be 120572 ge 04 as an example When the attributemembership function is greater than or equal to 120572 thisattribute value is ldquo1rdquo otherwise the value is ldquo0rdquoTherefore theresult of the foregoing example is one-dimensional Differentparticipants may have different feelings towards the degreeof the sufficiency of attributes so the questionnaire takes theindividual with the highest frequency When the final scoresare the same and cannot be distinguished the priority ofevaluation is119872 ≻ 119874 ≻ 119860 ≻ 119868 [69]

In the next stage the attractive factors are transformedinto evaluation criteria and the fuzzy AHP is used to simplifythe complex system into a logical hierarchical relationship

and integrate the individual opinions of the expert group inorder prioritize criteria Typically the fuzzy AHP consists ofthe following six steps

(1) Build the hierarchical structure of evaluation criteria(2) Build the fuzzy judgment matrix 119860 and compute the

weight vector 119903119894Based on the questionnaires we use TFNs (Table 3) to

express expertsrsquo assessments regarding the relative impor-tance of the criteria The linguistic scale is represented bythe nine-point scale where ldquoequally importantrdquo ldquoslightlyimportantrdquo ldquoimportantrdquo ldquovery importantrdquo and ldquoextremelyimportantrdquo are given the values 1 3 5 7 and 9 respectivelyand the values 2 4 6 and 8 are intermediate (Figure 4) Thefuzzy pairwise comparison matrix 119860 is built and the expertsrsquoweight vector 119903119894 towards criteria is obtained as the geometricmean as shown in

119860 =[[[[[[[

1 11988612 sdot sdot sdot 119886111989911988621 1 sdot sdot sdot 1198862119899 d

1198861198991 1198861198992 sdot sdot sdot 1

]]]]]]]=[[[[[[[[[[

1 11988612 sdot sdot sdot 1198861119899111988612 1 sdot sdot sdot 1198862119899 d11198861119899

11198862119899 sdot sdot sdot 1

]]]]]]]]]]

(3)

119903119894 = ( 119899prod119895=1

119886119894119895)1119899

= [1198861198941 otimes sdot sdot sdot otimes 119886119894119899]1119899 (4)

According to the characteristics of TFNs and the exten-sion theorem for two triangular fuzzy numbers 1 =(1198971 1198981 1199061) and1198722 = (1198972 1198982 1199062) the operations are as follows[70 71]


Figure 4 Linguistic scale in fuzzy AHP

Addition oplus1 oplus 2 = (1198971 1198981 1199061) oplus (1198972 1198982 1199062)

= (1198971 + 1198972 1198981 + 1198982 1199061 + 1199062) (5)

Multiplication otimes1 otimes 2 = (1198971 1198981 1199061) otimes (1198972 1198982 1199062)

= (1198971 otimes 1198972 1198981 otimes 1198982 1199061 otimes 1199062) (6)

Defuzzification

119863119890119891119906119911119911119910 (1) = 1003816100381610038161003816(1199061 minus 1198971) + (1198981 minus 1198971)10038161003816100381610038163 + 1198971 (7)

(3) Examine the consistency of the fuzzy judgmentmatrix119860According to the research of Buckley [55] we have the

following resultsSuppose 119860 = [119886119894119895] is a positive reciprocal matrix then119860 = [119886119894119895] is a fuzzy positive reciprocal matrix and if 119860 = [119886119894119895]

is consistent then 119860 = [119886119894119895] is also consistent Using this weknow whether the questionnaire is effective The consistentindexes (CI) and consistent ratios of the pairwise comparisonmatrices can be calculated as follows

119862119868 = (120582max minus 119899)(119899 minus 1)119862119877 = 119862119868119877119868

(8)

Here 120582max is the maximum eigenvalue of the pairwisecomparison matrix n is the number of criteria RI is therandom index (Table 4)The closer the CI to 0 the higher theconsistency When 119862119868 ≺ 010 we assume that the result haspassed the consistency test and can be included in the overalljudgment value

(4) Compute the fuzzy weight 119908119894 by normalization

119908119894 = 119903119894 otimes (119903119894 oplus sdot sdot sdot oplus 119903119898)minus1 (9)

(5) Defuzzification Use the center of area method in(7) to defuzzify the TFNs and find out the best nonfuzzy

performance value or the best crisp value to obtain theexplicit weight 119894 for each scheme

119894 = 119863119890119891119906119911119911119910 (119894) (10)

(6) Calculate the relative weight119882119894 for each criterionTherelative weight is the normalized value after defuzzification

119882119894 = 119894sum119899119894=1 119894 (11)

The fuzzy AHP has determined the raw weight values ofattractive factors criteria according to the intervieweesrsquoknowledge The procedure of adjusting weights includesunderstanding customersrsquo quality attributes towards differentfactors using the fuzzy Kano model questionnaire the aim ofwhich is to maximize the high return performance criteriaTan and Pawitra [72] suggest that designers should firstpay attention to attractive attributes then one-dimensionalattributes and at last must-be attributes before setting theadjustment coefficient 119870 as 4 2 and 1 respectively Theweight adjustment of the attractive factors can be calculatedas follows [50]

119908119894 119886119889119895 = 119882119894119870119894sum119899119894=1119882119894119870119894 (12)

In the equation 119908119894 119886119889119895 is the weight value adjusted forthe 119894119905ℎ element 119882119894 stands for the raw weight of the 119894119905ℎelement 119894 = 1 2 119899 and 119870119894 is the adjustment coefficientdetermined by the two-dimensional quality The priority ofattractive factors is determined using the weight adjustmentmethod by combining importance with satisfaction to useCRs analysis at the left side of the HoQ

33 Phase Three Evaluating the Relationship between CrucialAttractive Factors and Design Elements During the fuzzyQFD we first consider the CRs facet whose weight value iscalculated using the fuzzy AHP Next we decide the linkagestrength of the relationship between WHATS and HOWS Inthis study we assign the impact degree to the relation matrixaccording to the collection of expertsrsquo opinions and markit as Cohenrsquos [73] relation symbol (see Table 5) If there isno relation there is no mark means weak correlation Imeansmoderate correlation andmeans strong correlationThen we turn these marks into TFNs Every ECs has to pairwith at least one relation symbol of CRs otherwise it shouldbe abandoned

The basic theory of the fuzzy QFD is built on quantitativeand qualitative analysis The absolute value of the ECs ofthe HoQ 119860119868119895 can be obtained using the evaluation methodwhich is tomultiply the CRs weight by the correlation symbolof the correlation matrix and then sum these results Therelative weight 119877119868119895 is the normalized value of the absoluteweight

119860119868119895 = 119899sum119894=1

119882119894 otimes 119877119894119895 (13)

119877119868119895 = 119860119868119895sum119898119895=1 119860119868119895 (14)


Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151

Table 5 Relation matrix symbols and TFNs in fuzzy QFD

Symbol Definition Fuzzy numbersBlank No Relationship (0 0 0) Weak Relationship (1 3 5)I Medium Relationship (3 5 7) Strong Relationship (5 7 9)

where 119860119868119895 is the absolute importance of the 119895119905ℎ HOW119882119894 is the weight of the 119894119905ℎ WHAT 119877119894119895 is the degree of therelationship between the 119894119905ℎ WHAT and the 119895119905ℎ HOW 119894 =1 2 119899 119899 is the total number of CRs 119895 = 1 2 119898 and119898 is the total number of ECs

4 Case Study

In this section we choose minicars as the study subject Mini-cars have become popular due to small dimensions and lowfuel consumption Consumers can get in touch with minicarsfrequently in daily life so it is easy for them to get impressedand have deep understanding of the concept which is goodfor the survey Besides the manufacturing technologies ofthe automobile industry have become quite mature andthe process of purchasing a car by consumers has becomemore closely related to their considerations at the Kanseilevel Therefore the question of whether the appearance ofminicars meets customersrsquo psychological feelings has greatinfluence on consumer desires In what follows we apply theproposed method to minicars although it is also applicableto other product design fields

41 Phase One Assessing the Attractive Factors ofMinicars 95minicars from 2012 to 2017 are collected from professionalbooks magazines and the Internet as a sample The sampleis presented as 45 degree side photos of 297mmtimes210mmThe backgrounds and logos of the cars are excluded to reduceinterference

This study is divided into two parts the EGM qualitativeinterview and quantitative questionnaire 10 experts (5 malesand 5 females) with more than 3-year driving experience and5-year design experience act as interviewees in the EGMfuzzy AHP and fuzzy QFDThe fuzzy Kano model question-naire is released through the Internet 150 interviewees (75males and 75 females) from 25 to 50 years old are invited

After interviewing the participants regarding their feel-ings towards the pictures and analyzing the features of theEGM we obtain 33 abstract reasons (upper-level) 10 originalevaluation items (middle-level) and 140 concrete conditions(lower-level) Too many abstract reasons prevent consumersfrom distinguishing styles and images Accordingly we usethe KJ method to group the factors 5 experienced designerssimplify the 33 words to 7 representative upper-level abstract

reasons (Figure 5) Namely the attractive factors of minicarsare ldquosmall and cuterdquo ldquolight and livelyrdquo ldquofashionable and taste-fulrdquo ldquoappealing and delicaterdquo ldquonoble and elegantrdquo ldquohard andburlyrdquo and ldquocomfortable and ventilaterdquo Figures in bracketsrepresent the numbers of times the descriptions appearedTaking ldquofashionable and tastefulrdquo as an example we draw thecorresponding hierarchical structure (Figure 6)

42 Phase Two Determining Crucial Attractive Factors Con-sumersrsquo feelings towards products are multiple-criteria char-acteristics However it is truly hard for designers to measurethe relationship between performance criteria and customersatisfaction based on their subjective experiences Thereforethis study uses the fuzzy set theory combined with the Kanomodel to better identify the 7 quality attributes of minicars

The study uses a two-side questionnaire to ask intervie-wees about their satisfaction related to Kansei quality Fromthe classification results presented in Table 6 ldquofashionableand tastefulrdquo and ldquoappealing and delicaterdquo are most attractiveto consumers when they select minicars The 4 attributesldquosmall and cuterdquo ldquolight and livelyrdquo ldquohard and burlyrdquo andldquocomfortable and ventilaterdquo are one-dimensional having alinear relation with customer satisfaction ldquoNoble and ele-gantrdquo is amust-be attribute as the lack of it leads to customersrsquostrong dissatisfaction

In this research 7 attractive factors are used as evaluationcriteria According to ((3)-(11)) to calculate the 10 expertsrsquonormalized weightsW we take the sum of the criteria relativeweights to be 1 Wi stands for the arithmetic average value ofthe 10 expertsrsquo values which is shown in Table 7

Since the importance of a criterion provided by the fuzzyAHP is a one-dimensional concept it cannot recognize dif-ferent effects on customer satisfaction The two-dimensionalquality of the fuzzy Kano model helps to understand whatcustomers really need and support designers by providing animportant reference onwhat criteria to develop first With thehelp of (12) we obtain the adjusted weights correspondingto raw weights and the attributive classification of the 7attractive factors which is shown in Table 8

43 Phase Three Transforming Design Elements Accordingto the normalized weight values presented in Table 8 theattractive factor ldquofashionable and tastefulrdquo ranks the highestThe words in its group are ldquofashionablerdquo ldquotechnologicalrdquoldquomodernrdquo ldquotastefulrdquo ldquofuturisticrdquo and ldquowiserdquoWe obtain theirweights and sequence using the fuzzyAHP and then use themin the CRs at the left side of the HoQ where the correspond-ing original evaluation item of ldquofashionable and tastefulrdquoshown in Figure 6 and concrete conditions are imported tothe ECs of theHoQWedetermine the strength of the associa-tion between theCRs and ECs usingmatrix notation andmapthe incidence matrix table (Figure 7) before determining thespecific design elements and design focus using ((13)-(14))


Figure 5 Hierarchical structure of minicarsrsquo attractive factors

Abstract concreteUpper-level Middle-level Lower-level

(Kansei words) (original evaluation item) (design elements)

A vehicle shape

B headlights

C wheel hubs

fashionable and tasteful D rear view mirrors

E car grille

F air intakes

G fog lamp

1 sports car shaped

2 no obvious offset plane

3 car roofs and the whole glass of front windows

1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat

3 large side of the car spoke

＄1 with colors fitting bodies

＄2 bean sprout-liked water drop- shaped

＄3 organic-shaped

1 blend in with headlights

2 inverted trapezoidal

3 crisscrossed large grids

1 wave and ripple-shaped

2 blend in with front faces

3 vast scale

1 be corresponding to the shape of headlights

2 granular lamp band

3 silver metal edges

Figure 6 The corresponding hierarchical diagram of ldquofashionable and tastefulrdquo


Table 6 The fuzzy Kano model classification of abstract attractive factors

Factors M O I A R Q Total () CategorySmall and cute 17 47 23 10 3 0 100 OLight and lively 19 31 22 27 1 0 100 OFashionable and tasteful 6 17 38 39 0 0 100 AAppealing and delicate 3 15 29 51 2 0 100 ANoble and elegant 43 34 15 8 0 0 100 MHard and burly 28 36 24 12 0 0 100 OComfortable and ventilate 19 55 14 12 0 0 100 ONotes A attractive O one-dimensional M must-be I indifference and R reversal

Table 7 Ten expertsrsquo raw weights

Factors 1 2 3 4 5 6 7 8 9 10 Wi WSmall and cute 0018 0097 0042 0090 0025 0019 0032 0020 0019 0335 0070 0070Light and lively 0076 0094 0233 0129 0081 0071 0089 0042 0089 0193 0110 0110Fashionable and tasteful 0419 0054 0058 0065 0409 0267 0242 0115 0195 0118 0194 0194Appealing and delicate 0061 0213 0122 0045 0049 0133 0439 0106 0377 0081 0163 0163noble and elegant 0254 0028 0160 0234 0204 0038 0066 0409 0092 0023 0151 0151Hard and burly 0035 0071 0059 0079 0038 0060 0030 0062 0037 0182 0065 0065Comfortable and ventilate 0137 0443 0327 0359 0195 0413 0102 0246 0193 0068 0248 0248CI 0093 0087 0048 0068 0062 0083 0075 0075 0080 0089

Table 8 Adjusted weights

Factors Attributes classification Adjustment coefficient Weights Adjusted weights Normalized weights RankSmall and cute O 2 0070 0140 0055 6Light and lively O 2 0110 0220 0086 4Fashionable and tasteful A 4 0194 0776 0303 1Appealing and delicate A 4 0163 0652 0254 2Noble and elegant M 1 0151 0151 0059 5Hard and burly O 2 0065 0130 0051 7Comfortable and ventilate O 2 0248 0496 0193 3

44 Discussion In this study we use the EGM to assess theattractive factors of minicars and use the matrix deploymentof the fuzzy QFD to discover the relationship between cus-tomersrsquo emotional preferences and minicarrsquo design elementsThe numbers of times the 7 attractive factors mentioned bythe experts are not the same ldquoSmall and cuterdquo is the most fre-quently mentioned word (38 times) and ldquonoble and elegantrdquois the leastmentioned one (13 times)We analyze this from theperspective of the traditional Miryoku engineering and drawthe conclusion that ldquosmall and cuterdquo is what mostly affectsconsumersrsquo Kansei evaluation while ldquonoble and elegantrdquo is ofthe least importance In other words we should first focuson the ldquosmall and cuterdquo attribute when designing minicars toaddress customersrsquo emotional demands

However we can learn from the statistics of the secondstage that the final weight of ldquosmall and cuterdquo ranks the sixthThe reason for such difference is that the number of mentionsdoes not always correspond to importance When consumerschoose minicars they have certain expectations regarding itbeing small so naturally ldquosmall and cuterdquo is in their mindwhile in reality it cannot inspire their purchasing desire

In addition ldquosmall and cuterdquo has been defined as a one-dimensional attribute as is shown in the quality classificationresults of the fuzzy Kano model in Table 6 That is to sayquality performance is in direct proportion to customersatisfaction Nevertheless ldquoappealing and delicaterdquo with 28mentions and ldquofashionable and tastefulrdquo with 27 mentionshave been classified as attractive qualities They can catchcustomersrsquo attention and are easier to become competitiveadvantages

The top 3 attractive factors are ldquofashionable and tastefulrdquo(303) ldquoappealing and delicaterdquo (254) and ldquocomfortableand ventilaterdquo (193) with the total weight of 75This con-clusion shows that the 3 factors should be given full attentionwhen designing a minicar Consumers usually expect thatthe minicar design matches the trend of the times reflectspersonal tastes and is accompanied by delicate details At thesame time regardless of how small a minicar is it also needsto be comfortable and inviting and not depressing visuallyThe remaining four attractive factors include ldquolight and livelyrdquo(86) ldquonoble and elegantrdquo (59) ldquosmall and cuterdquo (55)and ldquohard and burlyrdquo (51) with the total weight value of


A vehicle shape B headlights C wheel hubs D rear viewmirrors

E car grille F air intakes G fog lamp

CRs importance rank

fashionable 0075 6

technological 0202 2

modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3

strong correlation mid correlation weak correlation

Figure 7 Incidence matrix table of ldquofashionable and tastefulrdquo in fuzzy QFD

lower than 10They are quite below the weight values of thetop 3 factors so we can know that interviewees do not caremuch about whether these 4 factors are present in a minicarldquoHard and burlyrdquo is essential to cars as consumers feelrelieved due to the safety level of the car Moreover minicarshave small sizes and are easy to park whichmakes the feelingsof ldquolight and livelyrdquo and ldquosmall and cuterdquo quite natural buteven if we focus on them customer satisfaction will not beenhanced largely At the same time the participants think thatthe main function of a minicar is to substitute walking sothere is no need to focus on ldquonoble and elegantrdquo Based on theresults the use of the fuzzy Kano model combined with thefuzzy AHP to adjust weights which is the research method ofthis study is suitable for discovering the essential propertiesof customersrsquo Kansei demands

In the next stage we analyze every attractive factor inturn to find out what design elements are effective to someattractive factors First we import ldquofashionable and tastefulrdquointo the incidence matrix of the fuzzy QFD to assess thedetailed design elements of CRs As can been seen from theresults if we want to meet ldquofashionable and tastefulrdquo criterionin the design of a minicar we should consider the elementswith the top 4 weights A3 F1 B3 and G2 These elementsare the design focus of the matrix deployment Moreoverit is better to avoid the form features such as E2 G1 C1and D3 when designing a minicar as otherwise the productsrsquoperformance in terms of ldquofashionable and tastefulrdquo will bereduced Based on the evaluation results in terms of the formconsumers think that A3 should be awarded the highest totalpoints having theweight value of 88 Regarding headlights

B3 has the highest weight of 78 For wheel hubs C3 hasthe highest weight of 36 for rear view mirrors D2 hasthe highest weight of 54 and for grille E1 has the highestweight of 4 F1 (81) and G2 (74) are the top 2 factorsfor air intakes and fog lamp The combination of the designfeatures mentioned above is what customers expect from aminicar that is ldquofashionable and tastefulrdquo Compared to otherforms and styles these factors are more appealing Thereforefuture designers can refer to this result to design the bestappearance of a minicar and address closely consumersrsquodemands and aesthetics Using the process and approachproposed in this study future designers can also establishcommunication media with customers to design high-qualityproducts and enhance customer satisfaction

5 Conclusion

Driven by the global competition and customer demandsmanufacturers spare no efforts in new product developmentto maintain competitive advantages Therefore the main goalof this study is to build a systematic method to evaluatecustomersrsquo attractive factors and use them in distinctivenew product design Taking minicars as an example thearticle obtains 7 attractive factors using the EGM of expertsrsquoopinions where ldquofashionable and tastefulrdquo and ldquoappealingand delicaterdquo turn out to have the largest weights At thesame time they are defined as attractive attributes in thetwo-dimensional quality classification which reflects theirstatus as the improvement focus of both experts and cus-tomers Giving priority to them will significantly promote


customersrsquo satisfaction ldquoComfortable and ventilaterdquo ldquolightand livelyrdquo ldquonoble and elegantrdquo ldquosmall and cuterdquo and ldquohardand burlyrdquo are ranked right after them and they also helpto avoid dissatisfaction due to the lack of quality attributesFurther design methods that improve attractive factors canbe obtained using the HoQ matrix which can provide a ref-erence for enterprises at early stages so that they can improveproduct competitiveness while creating market opportuni-ties In addition unlike most previous studies using the tradi-tional Miryoku engineering our proposed integrated meth-od shows good ability to capture effectively and accuratelycustomersrsquo real needs translating them into attractive prod-uct form elements Most importantly it has the followingadvantages

(i) The EGM extracts customersrsquo attractive factors accu-rately and rapidly It also builds the hierarchical rela-tionship between the factors and design elements

(ii) The fuzzy Kano model combined with the fuzzy AHPweight method explores the relationship betweenattractive factors and customer satisfaction to deter-mine the development priority of these factors

(iii) The corresponding hierarchical diagram of these cru-cial attractive factors is imported into incidence ma-trices separately which allows obtaining vital designelements that help designers develop attractive prod-uct forms to facilitate sales

(iv) We adopt fuzzy questionnaires in the experimentusing TFNs to express intervieweesrsquo subjective eval-uation and show what is really in their minds

There are limitations of this research as follows Firstwith the development of science and technology customersrsquopreferences can be easily influenced by the trends of timeOlder products no longer suit new customer needs Secondthis paper does not group interviewees due to the limitationsof time and space Third although customer preferences areanalyzed in this paper customer orientation should obtain awider view such as the consumption ability and consumingbehaviors as well as other factors In the future customersrsquoattractive factors should be kept updated to meet designtrends Furthermore we suggest carrying out segmentationstudies in differentmarkets using demographic variables suchas the gender age professional background and lifestyle

Conflicts of Interest

The authors declare that there are no conflicts of interest re-garding the publication of this paper

References

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[2] J C Narver S F Slater and B Tietje ldquoCreating a market orien-tationrdquo Journal ofMarket-FocusedManagement vol 2 no 3 pp241ndash255 1998

[3] DA Norman Emotional DesignWhyWeLove (or Hate) Every-dayThings Basic Books New York NY USA 2004

[4] J W Newman Motivation Research and Marketing Manage-ment Harvard University Graduate School of Business Divi-sion of Research Boston MA USA 1957

[5] PW Jordan Designing Pleasurable Products An Introduction tothe NewHuman Factors Taylor and Francis London UK 2000

[6] R JensenThe Dream Society How the Coming Shift from Infor-mation to Imagination Will Transform Your Business McGraw-Hill New York NY USA 1999

[7] K-C Wang ldquoUser-oriented product form design evaluationusing integrated kansei engineering schemerdquo Journal of Conver-gence Information Technology vol 6 no 6 pp 420ndash438 2011

[8] MNagamachi ldquoKansei engineering a new ergonomic consum-er-oriented technology for product developmentrdquo InternationalJournal of Industrial Ergonomics vol 15 no 1 pp 3ndash11 1995

[9] C Tanoue K Ishizaka and M Nagamachi ldquoKansei Engineer-ing a study on perception of vehicle interior imagerdquo Interna-tional Journal of Industrial Ergonomics vol 19 no 2 pp 115ndash1281997

[10] S Schutte and J Eklund ldquoDesign of rocker switches for work-vehiclesmdashan application of Kansei Engineeringrdquo Applied Ergo-nomics vol 36 no 5 pp 557ndash567 2005

[11] J J Dahlgaard and M Nagamachi ldquoPerspectives and the newtrend of Kanseiaffective engineeringrdquo The TQM Journal vol20 no 4 pp 290ndash298 2008

[12] J Vieira J M A Osorio S Mouta et al ldquoKansei engineeringas a tool for the design of in-vehicle rubber keypadsrdquo AppliedErgonomics vol 61 pp 1ndash11 2017

[13] M Ujigawa ldquoThe evolution of preference-based designrdquo Re-search and Development Institute vol 46 pp 1ndash10 2000

[14] J Park J-H Kim E-J Park and S M Ham ldquoAnalyzing userexperience design of mobile hospital applications using theevaluation grid methodrdquo Wireless Personal Communicationsvol 91 no 4 pp 1591ndash1602 2016

[15] K Shen K Chen C Liang W Pu and M Ma ldquoMeasuring thefunctional and usable appeal of crossover B-Car interiorsrdquoHuman Factors and Ergonomics in Manufacturing amp ServiceIndustries no 1 pp 106ndash122 2015

[16] C-HHo andK-CHou ldquoExploring the attractive factors of appiconsrdquo KSII Transactions on Internet and Information Systemsvol 9 no 6 pp 2251ndash2270 2015

[17] K-S Shen ldquoMeasuring the sociocultural appeal of SNS gamesin Taiwanrdquo Internet Research vol 23 no 3 pp 372ndash392 2013

[18] K-H Chen K-S Shen and M-Y Ma ldquoThe functional andusable appeal of Facebook SNS gamesrdquo Internet Research vol22 no 4 pp 467ndash481 2012

[19] M-YMa andL-T Y Tseng ldquoApplyingMiryoku (attractiveness)engineering for evaluation of festival industryrdquo Advances inInformation Sciences and Service Sciences vol 4 no 1 pp 1ndash92012

[20] M-Y Ma Y-C Chen and S-R Li ldquoHow to build design stra-tegy for attractiveness of new products (DSANP)rdquo Advances inInformation Sciences and Service Sciences vol 3 no 11 pp 17ndash26 2011

[21] M Nagamachi Y Okazaki andM Ishikawa ldquoKansei engineer-ing and application of the rough sets modelrdquo Proceedings of theInstitution of Mechanical Engineers Part I Journal of Systemsand Control Engineering vol 220 no 8 pp 763ndash768 2006

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[23] G A KellyThe Psychology of Personal Constructs Norton NewYork NY USA 1955

[24] J Sanui and M Inui ldquoPhenomenological approach to theevaluation of places a study on the construct system associatedwith place evaluationrdquo Journal of Architectural Planning andEnvironmental Engineering vol 367 pp 15ndash22 1986

[25] Y Akao Quality Function Deployment Integrating CustomerRequirements into Product Design Productivity Press Cam-bridge UK 1990

[26] J J Cristiano J K Liker and C CWhite III ldquoKey factors in thesuccessful application of quality function deployment (QFD)rdquoIEEE Transactions on Engineering Management vol 48 no 1pp 81ndash95 2001

[27] M Kowalska M Pazdzior and A Krzton-Maziopa ldquoImple-mentation of QFD method in quality analysis of confectioneryproductsrdquo Journal of Intelligent Manufacturing vol 29 pp 439ndash447 2018

[28] L-X Wang ldquoA new look at type-2 fuzzy sets and type-2 fuzzylogic systemsrdquo IEEE Transactions on Fuzzy Systems vol 25 no3 pp 693ndash706 2017

[29] A Martı Bigorra and O Isaksson ldquoCombining customer needsand the customerrsquos way of using the product to set customer-focused targets in the House of Qualityrdquo International Journalof Production Research vol 55 no 8 pp 2320ndash2335 2017

[30] W Jia Z Liu Z Lin C Qiu and J Tan ldquoQuantification for theimportance degree of engineering characteristics with a multi-level hierarchical structure in QFDrdquo International Journal ofProduction Research vol 54 no 6 pp 1627ndash1649 2015

[31] H Raharjo M Xie and A C Brombacher ldquoA systematicmethodology to deal with the dynamics of customer needs inQuality Function Deploymentrdquo Expert Systems with Applica-tions vol 38 no 4 pp 3653ndash3662 2011

[32] L-K Chan and M-L Wu ldquoQuality function deployment aliterature reviewrdquoEuropean Journal ofOperational Research vol143 no 3 pp 463ndash497 2002

[33] L R Guinta and N C Praizler The QFD Book The Team Ap-proach to Solving Problems and Satisfying Customers throughQuality Function Deployment Amacom New York NY USA1993

[34] J R Hauser and D Clausing ldquoThe house of qualityrdquo HarvardBusiness Review vol 66 no 3 pp 63ndash73 1988

[35] L P Khoo and N C Hot ldquoFramework of a fuzzy quality func-tion deployment systemrdquo International Journal of ProductionResearch vol 34 no 2 pp 299ndash311 1996

[36] L-K Chan and M-L Wu ldquoA systematic approach to qualityfunction deployment with a full illustrative examplerdquo Omega vol 33 no 2 pp 119ndash139 2005

[37] QWu ldquoFuzzymeasurable house of quality and quality functiondeployment for fuzzy regression estimation problemrdquo ExpertSystems with Applications vol 38 no 12 pp 14398ndash14406 2011

[38] S Zaim M Sevkli H Camgoz-Akdag O F Demirel A YesimYayla andDDelen ldquoUse ofANPweighted crisp and fuzzyQFDfor product developmentrdquoExpert SystemswithApplications vol41 no 9 pp 4464ndash4474 2014

[39] L-Z Lin H-R Yeh and M-C Wang ldquoIntegration of Kanorsquosmodel into FQFD for Taiwanese Ban-Doh banquet culturerdquoTourism Management vol 46 pp 245ndash262 2015

[40] S Vinodh K J Manjunatheshwara S Karthik Sundaram andV Kirthivasan ldquoApplication of fuzzy quality function deploy-ment for sustainable design of consumer electronics productsa case studyrdquo Clean Technologies and Environmental Policy vol19 no 4 pp 1021ndash1030 2017

[41] N KanoN Seraku F Takahashi and S Tsuji ldquoAttractive qualityandmust-be qualityrdquo Journal of The Japanese Society for QualityControl vol 14 no 2 pp 147ndash156 1984

[42] C Berger R Blauth D Boger et al ldquoKanorsquosmethods for under-standing customer-defined qualityrdquo The Center for Quality ofManagement Journal vol 2 no 4 pp 3ndash36 1993

[43] H C Yadav R Jain A R Singh and P K Mishra ldquoKano inte-grated robust design approach for aesthetical product design acase study of a car profilerdquo Journal of Intelligent Manufacturingvol 28 no 7 pp 1709ndash1727 2017

[44] N Velikova L Slevitch and K Mathe-Soulek ldquoApplicationof Kano model to identification of wine festival satisfactiondriversrdquo International Journal of Contemporary HospitalityManagement vol 29 no 10 pp 2708ndash2726 2017

[45] P Ji J Jin T Wang and Y Chen ldquoQuantification and integra-tion of Kanorsquos model into QFD for optimising product designrdquoInternational Journal of Production Research vol 52 no 21 pp6335ndash6348 2014

[46] C-HWang and O-Z Hsueh ldquoA novel approach to incorporatecustomer preference and perception into product configura-tion a case study on smart padsrdquo Computer Standards ampInterfaces vol 35 no 5 pp 549ndash556 2013

[47] MHartono andTK Chuan ldquoHow theKanomodel contributesto Kansei engineering in servicesrdquo Ergonomics vol 54 no 11pp 987ndash1004 2011

[48] C Llinares and A F Page ldquoKanorsquos model in Kansei Engineeringto evaluate subjective real estate consumer preferencesrdquo Inter-national Journal of Industrial Ergonomics vol 41 no 3 pp 233ndash246 2011

[49] A M M Sharif Ullah and J Tamaki ldquoAnalysis of Kano-model-based customer needs for product developmentrdquo Systems Engi-neering vol 14 no 2 pp 154ndash172 2011

[50] C C Chen andMC Chuang ldquoIntegrating theKanomodel intoa robust design approach to enhance customer satisfaction withproduct designrdquo International Journal of Production Economicsvol 114 no 2 pp 667ndash681 2008

[51] Y-C Lee and S-Y Huang ldquoA new fuzzy concept approach forKanorsquos modelrdquo Expert Systems with Applications vol 36 no 3pp 4479ndash4484 2009

[52] H C Yadav R Jain S Shukla S Avikal and P K Mishra ldquoPri-oritization of aesthetic attributes of car profilerdquo InternationalJournal of Industrial Ergonomics vol 43 no 4 pp 296ndash3032013

[53] C-H Wang ldquoIncorporating customer satisfaction into thedecision-making process of product configuration a fuzzy kanoperspectiverdquo International Journal of Production Research vol51 no 22 pp 6651ndash6662 2013

[54] C-H Wang and J Wang ldquoCombining fuzzy AHP and fuzzyKano to optimize product varieties for smart cameras a zero-one integer programming perspectiverdquoApplied Soft Computingvol 22 pp 410ndash416 2014

[55] M G Violante and E Vezzetti ldquoKano qualitative vs quantitativeapproaches an assessment framework for products attributesanalysisrdquo Computers in Industry vol 86 pp 15ndash25 2017

[56] T L Saaty The Analysis Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill NewYork NY USA1980

[57] G-N Zhu J Hu J Qi C-CGu and Y-H Peng ldquoAn integratedAHP andVIKOR for design concept evaluation based on roughnumberrdquo Advanced Engineering Informatics vol 29 no 3 pp408ndash418 2015


[58] Y Sato K H Tan and Y K Tse ldquoAn integrated marginal anal-ysis approach for build-to-order productsrdquo International Jour-nal of Production Economics vol 170 pp 422ndash428 2015

[59] J Ooi M A B Promentilla R R Tan D K S Ng and N GChemmangattuvalappil ldquoA systematic methodology for multi-objective molecular design via analytic hierarchy processrdquoProcess Safety and Environmental Protection vol 111 pp 663ndash677 2017

[60] L A Zadeh ldquoFuzzy setsrdquo Information and Computation vol 8pp 338ndash353 1965

[61] P J M Laarhoven andW Pedrycz ldquoA fuzzy extension of Saatyrsquospriority theoryrdquo Fuzzy Sets and Systems vol 11 no 1ndash3 pp 229ndash241 1983

[62] J J Buckley ldquoFuzzy hierarchical analysisrdquo Fuzzy Sets andSystems vol 17 no 3 pp 233ndash247 1985

[63] S E Alptekin ldquoA fuzzy decision support system for digital cam-era selection based on user preferencesrdquo Expert Systems withApplications vol 39 no 3 pp 3037ndash3047 2012

[64] J Cho and J Lee ldquoDevelopment of a new technology productevaluation model for assessing commercialization opportuni-ties using Delphi method and fuzzy AHP approachrdquo ExpertSystems with Applications vol 40 no 13 pp 5314ndash5330 2013

[65] T-M Yeh F-Y Pai and C-W Liao ldquoUsing a hybrid MCDMmethodology to identify critical factors in new product devel-opmentrdquo Neural Computing and Applications vol 24 no 3-4pp 957ndash971 2014

[66] M Sabaghi C Mascle P Baptiste and R Rostamzadeh ldquoSus-tainability assessment using fuzzy-inference technique (SAFT)a methodology toward green productsrdquo Expert Systems withApplications vol 56 pp 69ndash79 2016

[67] M-D Shieh Y Li and C-C Yang ldquoProduct form designmodel based on multiobjective optimization and multicriteriadecision-makingrdquo Mathematical Problems in Engineering vol2017 Article ID 5187521 15 pages 2017

[68] K Matzler and H H Hinterhuber ldquoHow to make productdevelopment projects more successful by integrating Kanorsquosmodel of customer satisfaction into quality function deploy-mentrdquo Technovation vol 18 no 1 pp 25ndash38 1998

[69] CQM ldquoA special issue on Kanos methods for understandingcustomer defined qualityrdquoCenter ForQualityManagement Jour-nal vol 2 no 4 pp 3ndash35 1993

[70] L A Zadeh ldquoThe concept of a linguistic variable and its appli-cation to approximate reasoning Irdquo Information Sciences vol 8no 3 pp 199ndash249 1975

[71] J-Y Teng and G-H Tzeng ldquoFuzzy multicriteria ranking ofurban transportation investment alternativesrdquo TransportationPlanning and Technology vol 20 no 1 pp 15ndash31 1996

[72] KCTan andTA Pawitra ldquoIntegrating SERVQUALandKanorsquosmodel intoQFD for service excellence developmentrdquoManagingService Quality vol 11 no 6 pp 418ndash430 2001

[73] L Cohen Quality Function Deployment How to Make QFDWork for You Addison Wesley Massachusetts MA USA 1995

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purchase decisions Nagamachi a Japanese scholar first sug-gested taking into account customersrsquo senses and feelings(Kansei) in the 1970s Further Kansei engineering was de-veloped as a comprehensive consumer oriented technologyfor the NPD [8] Researchers consider five senses and theirquantification to study consumersrsquo emotions Tanoue et al [9]utilize Kansei engineering to evaluate automotive interiorimages especially roominess and oppressiveness Schutte andEklund [10] apply Kansei engineering to design rockerswitches for work-vehicles Nagamachi [11] presents and dis-cusses Kansei engineering thus realizing several new Kanseiproducts so far Vieira et al [12] use Kansei engineeringfor the design of in-vehicle rubber keypads Miryoku engi-neering is a part of Kansei engineering since 1998 when theJapan Society of Kansei Engineering (JSKE) was establishedIt mainly discovers user preferences which come from prod-uct values and charms determined by product attributes [13]Kansei engineering quantifies humanrsquos perception and feelingwith the method of engineering and then transforms con-sumersrsquo feeling into the technology of new products How-everMiryoku engineering discusses the relationship betweenconsumersrsquo abstract reasons for specific products and con-crete characteristics with the qualitative way of face-to-faceinterview Moreover the personal hierarchical diagram isformed by laddering ldquoMiryokurdquo in Japanese means ldquothepower of attractivenessrdquo Product attractiveness is primary toconsumers Based on in-depth interviewsMiryoku engineer-ing helps interviewees to take various appealing factors fromsamples and transfer the productrsquos Kansei attractiveness intoits form with the goal to improve customer satisfaction andmarket reception

As an important research method of Miryoku engineer-ing the objective of the evaluation grid method (EGM) is tobuild a hierarchical structure between abstract reasons andconcrete design elements which is usually hard to captureThe EGM has been successfully applied to product designgame development festival industries and other researchfields [14ndash20] Nevertheless there are two urgent issues thatneed to be solved First the screening of words among ob-tained upper-level attractive factors (Kansei words) is basedon their number of appearance by respondents where fre-quencies only show the degrees to which words are relatedto the product but do not reflect their importance Secondnumerical relationships between upper-level attractive fac-tors and lower-level design elements are usually based onmultiple linear regressions such as the Quantification TheoryType I (QT-I) used for mapping but Kansei data does notalways have linear features assumed when using the normaldistribution [21] A linear regression can lead to deviation andmisjudgment in the consequent design process

In the view of the above arguments the present studyintends to import the attractive factors of the EGM into thefuzzy quality function deployment (QFD) system to acquirethe best combination of crucial Kansei needs and designelements First we adopt the hierarchical diagram obtainedby the EGM to the House of Quality (HoQ) and secondwe obtain the crucial attractive factors by combining thetwo-dimensional fuzzy Kano model and the fuzzy analytichierarchy process (AHP) Design teams are recommended

to gather limited resources and pay attention to these resultsin order to maximize overall customer satisfaction They canexcavate the most important design elements by comparingcrucial attractive factors and design elements obtained bythe HoQ This study aims to discover customersrsquo feelingsand translate them into form elements that enhance positiveeffects and increase the attraction of product appearanceThe proposed method is based on the fuzzy theory usedfor uncertain consumer requirements (CRs) We build anobjective semantic questionnaire to learn real customer Kan-sei demands to cut losses resulting from colloquial phrasesFurthermore a brief comparison of the distinction betweenthis study and previous studies has been presented in Table 1The main contribution of this work can be summarized asfollows

(i) A hybrid framework is proposed to effectively convertthe voice of customers (attractive factors) into thevoice of engineers (design elements) thus reducingthe generation gap between both sides

(ii) Via combination of a two-dimensional model and theweight analysis method consumersrsquo attractive factorsto target products can be evaluated more accurately

(iii) Our survey data assist enterprises to identify custom-ersrsquo expectations thus finally integrating these intothe NPD

The remainder of this article is organized as followsSection 2 provides a brief literature review In Section 3 thenewly proposed method is explained in detail In Section 4 acase study for minicars is provided to demonstrate the effec-tiveness of the developed approach The paper is concludedin Section 5

2 Literature Review

21Miryoku Engineering and Evaluation GridMethod Miry-oku engineering proposed by a Japanese scholar MasatoUjigawa and his group in 1991 aims to create attractive pro-ducts and spaces by adopting a design philosophy centered atconsumer preferences [13] The charm traits of a product canbe captured through learning the way in which consumerschoose products and studying successful examples of productdesign so that an attractive design can be created [22] How-ever Miryoku is a vague concept that cannot be measuredby specific tools For this reason in this research we analyzecustomersrsquo attractive factors by layers with the aid of theEGM

The EGM is an important research approach of Miryokuengineering proposed by Japanese scholars Junichiro Sanuiand Masao Inui based on the psychologistrsquos Repertory GridMethod of Kelly in 1986 [23] with two added steps (Figure 1)One step compares and evaluates samples in terms of meritsand demerits during interviews to obtain original evaluationitems through intervieweesrsquo perspectives and senses Theother step obtains abstract reasons (upper-level) which con-nect usersrsquo emotion attitudes and product concrete conditions(lower-level) By doing this repeatedly we can obtain a hier-archical diagram of consumersrsquo inner feelings towards sam-ples [24]


Table1Abriefcom

paris

onof

thed


dyandprevious

studies

Publication

Expertinterview

Emotionalevaluation

Functio

nalevaluation

Transla

testechn

iques

Marketsegmentatio

nTh

ispaper

EGM

FuzzyKa

nomod

elFuzzy

AHP

FuzzyQFD

Shen

etal[15]

EGM

KEQT-I

Shen

[17]

EGM

QT-I

HoandHou

[16]

EGM

QT-I

Userb

ackgroun

dsWang[28]

AHPTR

IZQFD

Userp

erceptions

WangandHsueh

[46]

AHPKa

nomod

elDEM

ATEL

Custo

mer

perceptio

nsWangandWang[54]

FuzzyKa

nomod

elFuzzy

AHP

Affo

rdableprices

Chen

andCh

uang

[50]

KEK

anomod

elEntropy

QT-I

Alptekin[63]

Focusg

roup

FuzzyAHPFC

PUserp

rofiles

Yadavetal[52]

FuzzyKa

nomod

elQFD

Shiehetal[67]

POSFu

zzyAHP

QT-I

Wang[7]

KERS

TANFIS

Linetal[39]

Focusg

roup

Kano

mod

elFu

zzyQFD

Jietal[45]

Kano

mod

elQFD

NotesK

EKa

nseiengineeringTR


roblem

solvingDEM

ATEL

decision

-makingtrialand

evaluatio

nlabo

ratoryFCP

fuzzy

comprom

iseprogrammingPO

SPareto

optim

alsolutio

nsR

ST


aptiv

eNetwo

rk-Based

FuzzyInferenceS

ystem





































Sufficiency






119878 =[[[[[[[[[

0 0 007 021 0420 0 003 009 0180 0 0 0 00 0 0 0 00 0 0 0 0

]]]]]]]]]

(1)


119879 = 028119860 018119872 042119874 012119868 0119877 (2)







119860 =[[[[[[[


1198861198991 1198861198992 sdot sdot sdot 1

]]]]]]]=[[[[[[[[[[


11198862119899 sdot sdot sdot 1

]]]]]]]]]]

(3)

119903119894 = ( 119899prod119895=1

119886119894119895)1119899






= (1198971 + 1198972 1198981 + 1198982 1199061 + 1199062) (5)



Defuzzification

119863119890119891119906119911119911119910 (1) = 1003816100381610038161003816(1199061 minus 1198971) + (1198981 minus 1198971)10038161003816100381610038163 + 1198971 (7)




119862119868 = (120582max minus 119899)(119899 minus 1)119862119877 = 119862119868119877119868

(8)






119894 = 119863119890119891119906119911119911119910 (119894) (10)


119882119894 = 119894sum119899119894=1 119894 (11)


119908119894 119886119889119895 = 119882119894119870119894sum119899119894=1119882119894119870119894 (12)




119860119868119895 = 119899sum119894=1

119882119894 otimes 119877119894119895 (13)

119877119868119895 = 119860119868119895sum119898119895=1 119860119868119895 (14)


Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151




4 Case Study















A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018









Table1Abriefcom

paris

onof

thed


dyandprevious

studies

Publication

Expertinterview

Emotionalevaluation

Functio

nalevaluation

Transla

testechn

iques

Marketsegmentatio

nTh

ispaper

EGM

FuzzyKa

nomod

elFuzzy

AHP

FuzzyQFD

Shen

etal[15]

EGM

KEQT-I

Shen

[17]

EGM

QT-I

HoandHou

[16]

EGM

QT-I

Userb

ackgroun

dsWang[28]

AHPTR

IZQFD

Userp

erceptions

WangandHsueh

[46]

AHPKa

nomod

elDEM

ATEL

Custo

mer

perceptio

nsWangandWang[54]

FuzzyKa

nomod

elFuzzy

AHP

Affo

rdableprices

Chen

andCh

uang

[50]

KEK

anomod

elEntropy

QT-I

Alptekin[63]

Focusg

roup

FuzzyAHPFC

PUserp

rofiles

Yadavetal[52]

FuzzyKa

nomod

elQFD

Shiehetal[67]

POSFu

zzyAHP

QT-I

Wang[7]

KERS

TANFIS

Linetal[39]

Focusg

roup

Kano

mod

elFu

zzyQFD

Jietal[45]

Kano

mod

elQFD

NotesK

EKa

nseiengineeringTR


roblem

solvingDEM

ATEL

decision

-makingtrialand

evaluatio

nlabo

ratoryFCP

fuzzy

comprom

iseprogrammingPO

SPareto

optim

alsolutio

nsR

ST


aptiv

eNetwo

rk-Based

FuzzyInferenceS

ystem





































Sufficiency






119878 =[[[[[[[[[

0 0 007 021 0420 0 003 009 0180 0 0 0 00 0 0 0 00 0 0 0 0

]]]]]]]]]

(1)


119879 = 028119860 018119872 042119874 012119868 0119877 (2)







119860 =[[[[[[[


1198861198991 1198861198992 sdot sdot sdot 1

]]]]]]]=[[[[[[[[[[


11198862119899 sdot sdot sdot 1

]]]]]]]]]]

(3)

119903119894 = ( 119899prod119895=1

119886119894119895)1119899






= (1198971 + 1198972 1198981 + 1198982 1199061 + 1199062) (5)



Defuzzification

119863119890119891119906119911119911119910 (1) = 1003816100381610038161003816(1199061 minus 1198971) + (1198981 minus 1198971)10038161003816100381610038163 + 1198971 (7)




119862119868 = (120582max minus 119899)(119899 minus 1)119862119877 = 119862119868119877119868

(8)






119894 = 119863119890119891119906119911119911119910 (119894) (10)


119882119894 = 119894sum119899119894=1 119894 (11)


119908119894 119886119889119895 = 119882119894119870119894sum119899119894=1119882119894119870119894 (12)




119860119868119895 = 119899sum119894=1

119882119894 otimes 119877119894119895 (13)

119877119868119895 = 119860119868119895sum119898119895=1 119860119868119895 (14)


Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151




4 Case Study















A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018












































Sufficiency






119878 =[[[[[[[[[

0 0 007 021 0420 0 003 009 0180 0 0 0 00 0 0 0 00 0 0 0 0

]]]]]]]]]

(1)


119879 = 028119860 018119872 042119874 012119868 0119877 (2)







119860 =[[[[[[[


1198861198991 1198861198992 sdot sdot sdot 1

]]]]]]]=[[[[[[[[[[


11198862119899 sdot sdot sdot 1

]]]]]]]]]]

(3)

119903119894 = ( 119899prod119895=1

119886119894119895)1119899






= (1198971 + 1198972 1198981 + 1198982 1199061 + 1199062) (5)



Defuzzification

119863119890119891119906119911119911119910 (1) = 1003816100381610038161003816(1199061 minus 1198971) + (1198981 minus 1198971)10038161003816100381610038163 + 1198971 (7)




119862119868 = (120582max minus 119899)(119899 minus 1)119862119877 = 119862119868119877119868

(8)






119894 = 119863119890119891119906119911119911119910 (119894) (10)


119882119894 = 119894sum119899119894=1 119894 (11)


119908119894 119886119889119895 = 119882119894119870119894sum119899119894=1119882119894119870119894 (12)




119860119868119895 = 119899sum119894=1

119882119894 otimes 119877119894119895 (13)

119877119868119895 = 119860119868119895sum119898119895=1 119860119868119895 (14)


Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151




4 Case Study















A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018











= (1198971 + 1198972 1198981 + 1198982 1199061 + 1199062) (5)



Defuzzification

119863119890119891119906119911119911119910 (1) = 1003816100381610038161003816(1199061 minus 1198971) + (1198981 minus 1198971)10038161003816100381610038163 + 1198971 (7)




119862119868 = (120582max minus 119899)(119899 minus 1)119862119877 = 119862119868119877119868

(8)






119894 = 119863119890119891119906119911119911119910 (119894) (10)


119882119894 = 119894sum119899119894=1 119894 (11)


119908119894 119886119889119895 = 119882119894119870119894sum119899119894=1119882119894119870119894 (12)




119860119868119895 = 119899sum119894=1

119882119894 otimes 119877119894119895 (13)

119877119868119895 = 119860119868119895sum119898119895=1 119860119868119895 (14)


Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151




4 Case Study















A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018









Table 4 RI Value

119899 1 2 3 4 5 6 7 8 9 10 11RI 000 000 058 090 112 124 132 141 145 149 151




4 Case Study















A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018












A vehicle shape

B headlights

C wheel hubs


E car grille

F air intakes

G fog lamp

1 sports car shaped



1 panda-like eyes

2 narrow arcs

3 LED lights inlays

1 turbine shaped

2 flat




＄3 organic-shaped






3 vast scale



















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018






















CRs importance rank

fashionable 0075 6


modern 0173 4

tasteful 0205 1

futuristic 0165 5

wise 0182 3

absolute weight

relative weight

rank

193

6

154

4

337

6

149

9

143

6

298

5

135

0

121

1

140

5

205

1

195

4

136

0

155

0

086

5

091

0

313

1

273

1

142

9

113

5

281

9

158

5

005

0

004

0

008

8

003

9

003

8

007

8

003

203

003

4

003

6

005

100

005

4

003

5

004

0

002

3

002

4

008

2

007

1

003

7

003

0

007

4

004

1

8 11 1 12 13 3 18 17 15 7 6 16 10 21 20 2 5 14 19 4 9

1 2 3 1 2 3 1 2 3 ＄1 ＄2 ＄3 1 2 3 1 2 3 1 2 3






5 Conclusion











References



















































































Journal of












Journal of







Volume 2018






Volume 2018

















References



















































































Journal of












Journal of







Volume 2018






Volume 2018




































































Journal of












Journal of







Volume 2018






Volume 2018








integrating evaluation grid method and fuzzy quality...

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